Note: artificial intelligence (AI) endows applications with the ability to automatically learn and adapt from experience via interacting with the surroundings/environment. See the blog “Artificial Intelligence is not Fake Intelligence” for a more detailed explanation on artificial intelligence and machine learning.

The Fast Company article “How to Stop Worrying and Love the Great AI War of 2018,” projected that the AI battle would ultimately boil down between the “AI Big 6”: Alphabet/Google, Amazon, Apple, Facebook, IBM, and Microsoft. However, there are other contenders worthy of consideration including GE, Tesla, Netflix, Baidu, Tencent, and Albaba.

But what are the characteristics of organizations that will be the ultimate winners in this Great AI War? What are the behaviors and actions that will distinguish those organizations that capitalize on this AI gold rush while others “fumble the future”?

I believe that the AI winners will have the following characteristics:

Users, not purveyors, of AI technology

Embrace open source for technology agility (independence)

Mastery of Big Data (and no, Big Data is not dead)

Let me state my case.

#1 Users, Not Purveyors, of AI Technology
The Market Capitalization Leaderboard shown in Figure 1 offers important clues as to which organizations will likely be the AI winners. What will set these organizations apart will be not the selling of technology, but their ability leverage AI for “value capture.”

Figure 1: Marketing Capitalization Leaders as of May 26, 2017.

By the way, I think Kleiner Perkins was lazy in classifying “Industry Segment.” The market leaders are less purveyors of AI technology than they are users of AI technology.

Less than 10% of Amazon’s revenue comes from technology (cloud); $12B in cloud revenue out of a total revenue of $136B in 2016. So what Industry Segment are they in?

Google had quarterly revenues (Q1, 2016) of $26B of which digital media/advertising (search) represented $23B. Their “other” businesses (including Google Cloud) were only $3B. So what Industry Segment are they in?

Apple’s most recent quarterly (Q3, 2016) revenues were $42B out of which the iPhone (personal communications, information and entertainment) and the associated iPhone ecosystem (iTunes, Apple Music, App Store) comprised an aggregated $37.5B.

Finally, I’m not aware of any AI or data technologies that Facebook sells to the general market. Facebook generated $9.3B in revenue in Q2, 2017 of which $9.16B came from Ad revenue. So what Industry Segment are they in?

Mastering Value Capture. Just having the technology is not sufficient; it’s how you use the technology to derive and then drive new sources of customer, business, operational, and financial value that matters. Ultimately, the AI war is about “value capture.”

The companies listed in Figure 1 are trying to dominate markets, not technology. For example:

Each of these AI leaders seeks to extend their value capture capabilities into new markets, including transportation (autonomous vehicles), healthcare, finance, media, and entertainment.

Other market leaders are also moving aggressively to exploit the power of AI to capture more customer, products and operational value. JPM Morgan (#11) is focused on building an AI platform (see “JPMorgan Takes AI Use to the Next Level”) that will allow JPMC to dominate financial trading. And GE (#16) has made a strategic bet with their Predix platform (see “GE’S Big Bet on Data and Analytics”) as the platform for dominating the Industrial Internet of Things.

Microsoft (#3) is the one exception as Microsoft is a purveyor of technology. But even Microsoft is branching beyond just selling technology into trying to dominate markets such as digital media, entertainment, and social media where their AI “chops” can give them competitive advantages (see “The Jewel of Microsoft’s Earnings”).

#2 Embrace Open Source for Technology Agility (Independence)
AI leaders will exploit open-source business models to establish platform dominance/standardization, and create technology agility and independence. They will develop an enabling technology, and then give it away via open source. This enables them to encourage the growing community of developers, especially those up-and-coming developers in universities and research labs, to build out and create de facto standards around their enabling technologies.

The leadership role that the “Great AI War” combatants are playing can be seen in many open source projects. For example, Torch is an open source machine learning library and scientific computing framework. The “official maintainers” of Torch are:

Research Scientist @ Facebook

Senior Software Engineer @ Twitter

Research Scientist @ Google DeepMind

Research Engineer @ Facebook

Training and Education. Another strategy from the Global AI leaders the creation of community or industry training and education opportunities around their open source technologies. For example, Google is committing $1 billion to train American workers to build new businesses with Google’s AI tools (see “Google Commits $1 Billion in Grants to Train U.S. Workers for High-Tech Jobs”).

Avoiding Technology Lock-in. But equally important is that these AI leaders are seeking to avoid technology and architecture lock-in. They have watched old school organizations struggle with proprietary software packages that took months if not years for upgrades and bug fixes, while paying a burdensome annual maintenance fees (33% of list price means you’re buying the entire software package again every 3 years). In a world where the enabling data and analytic technologies are changing nearly daily, technological and architecture agility (at scale) and independence is mandatory for organizations looking to win the Great AI War.

#3 Mastery of Big Data
Everyone knows about the astounding growth of big data over the last decade as organizations focused on capturing detailed customer, product, operational and market data. Initially fueled by commerce, web and social media data, big data has accelerated with the growth of video, wearables, and the Internet of Things. (See Figure 2).

However, organizations have struggled to monetize this wealth of data. Enter artificial intelligence.

Figure 2: Fueling the Insatiable Appetite for Data

More Data = Better AI. Artificial intelligence can exploit massive data sets to identify patterns on a scale that flummox traditional Business Intelligence “slice and dice” and query technologies. Data is the food that feeds AI. The more data the AI models consume, the smarter AI gets. For example, Facebook is mastering facial recognition via its DeepFace Deep Learning application by virtue of owning the world’s largest repository of photos.

To illustrate the symbiotic relationship between big data and AI, let’s look at autonomous vehicles (AV). AV require enormous quantities of data to feed the AV machine learning algorithms. It would take tens of thousands of hours of real-world driving data across a variety of driving scenarios to teach cars how to navigate on their own. To address this data volume problem, AV companies are using the video game “Grand Theft Auto” to help generate enough data in order to train Autonomous Vehicles (see “GTA is Teaching Self-Driving Cars How to Navigate Better in the Real World”).

Data Lake. Leading AI organizations are exploiting the data lake concept to not only store the growing wealth of structured and unstructured (internal and publicly-available) data, but to provide an elastic, scalable, self-provisioning data science platform for “collaborative value creation” in building the machine learning and artificial intelligence models (see “Data Lake Business Model Maturity Index” for more details on data lake business model maturation).

Exploiting the Economic Value of Data. Leading AI organizations realize that data and analytics are unlike any traditional corporate assets. Data and analytics are digital assets that never wear out, never deplete, and can be used simultaneously at near-zero marginal cost across an infinite business and operational use cases. Understanding the true economic value of the organization’s data can help to prioritize technology and business investments that accelerate value capture from these data sources (see University of San Francisco research paper “Determining the Economic Value of Data” for more details).

Conclusion: How to Become an AI Winner
As has been discussed many times in my blog series, and explored in detail in my book, “Big Data MBA: Driving Business Strategies with Data Science,” AI winners will ultimately be those organizations that are the most effective at leveraging data and analytics to power their business models (see Figure 3).

Figure 3: How Effective Is Your Organization at Leveraging Data and Analytics to Power Your Business Models?

So in conclusion, let’s have some fun with this blog and think outside of the box about some hypothetical scenarios in which companies exploit this AI gold rush:

What would be the business model ramifications to GE if they were to open source Predix and offer Predix training to universities and third party developers?

What would be the business model ramifications to JPMC if they were to open source their trading platform to universities and third party developers?

What would be the business model ramifications if IBM moved out of the technology purveyor business and instead acquired companies in financial services and healthcare where their Watson AI platform could create market dominance?

As the world prepares for the impending great AI war, now is not the time for organizations to be shy or to cling to old, outdated business models.

William Schmarzo is Chief Technology Officer, Dell EMC Global Services Big Data Practice at EMC. He is the author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”. He’s written white papers and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course.

William Schmarzo is Chief Technology Officer, Dell EMC Global Services Big Data Practice at EMC. He is the author of “Big Data: Understanding How Data Powers Big Business” and “Big Data MBA: Driving Business Strategies with Data Science”. He’s written white papers and is a frequent speaker on the use of Big Data and data science to power an organization’s key business initiatives. He is a University of San Francisco School of Management (SOM) Executive Fellow where he teaches the “Big Data MBA” course.

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